from io import BytesIO
from urllib.request import urlopen
import librosa
from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor

processor = AutoProcessor.from_pretrained("/models/other_models/qwen2audio7b_instruct")
model = Qwen2AudioForConditionalGeneration.from_pretrained("/models/other_models/qwen2audio7b_instruct", device_map="auto")

conversation = [
    {"role": "user", "content": [
        {"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/guess_age_gender.wav"},
    ]},
    {"role": "assistant", "content": "Yes, the speaker is female and in her twenties."},
    {"role": "user", "content": [
        {"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/translate_to_chinese.wav"},
        {"type": "text", "text": "说话人的年龄、情绪和性别是什么，以及她说了什么（完整内容，让你干嘛了）？，有没有骂人或者情绪不好？"},
    ]},
]
text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
audios = []
for message in conversation:
    if isinstance(message["content"], list):
        for ele in message["content"]:
            if ele["type"] == "audio":
                audios.append(librosa.load(
                    BytesIO(urlopen(ele['audio_url']).read()), 
                    sr=processor.feature_extractor.sampling_rate)[0]
                )

inputs = processor(text=text, audios=audios, return_tensors="pt", padding=True)
inputs.input_ids = inputs.input_ids.to("cuda")

generate_ids = model.generate(**inputs, max_length=256)
generate_ids = generate_ids[:, inputs.input_ids.size(1):]

response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print(response)

